A fundamental problem in the art of automatic document image processing relates to image defects, that is, imperfections in the image as compared to the original ideal artwork used to create the image. The sources of image defects are numerous and well known. For example, the original printed document (e.g., paper document) which was the source of the image may be defective (e.g., the paper has spots of dirt, folds, or was printed from a faulty printing device). Further, when the paper document was scanned, the paper may have been skewed while being placed in the scanner, resulting in a distortion of the image. In addition, the optics of the scanning process itself can produce defects due to, for example, vibration, pixel sensor sensitivity or noise.
The above-mentioned image defects result in poor display quality of the image, e.g. a facsimile image, and are a particular problem in document image processing because of the character recognition accuracy required in the automatic processing of documents. For example, optical character recognition (OCR) is often an integral part of an image processing system. OCR is the process of transforming a graphical bit image of a page of textual information into a text file which can be later edited, for example, using word processing software. As is well known in the art, image classifiers are key components of most OCR systems used for analyzing a digital representation of an image. The accuracy of such OCR system classifiers significantly decreases when the quality of the image source is degraded even slightly.
Therefore, a need exists for a technique for the enhancement of degraded document images to improve their display quality characteristics and image recognition accuracy.